def __call__(self, iteration, logdir, population, best, best_cost):
# don't write images if no improvement
if (self.best_so_far != -1 and best_cost >= self.best_so_far):
return
self.best_so_far = best_cost
#-TODO eliminate dependence on logdir
img_h, img_w = self.img_shape[:2]
decoded = np.empty((img_h, img_w, 3), dtype=np.int)
fitness_ext.decode_rectangles(best, decoded)
save_array_as_image(decoded, '%s/%04d.png' % (logdir, iteration))
def __call__(self, iteration, logdir,
population, global_best, global_best_cost):
# don't write images if no improvement
if (self.best_so_far != -1 and
global_best_cost >= self.best_so_far):
return
self.best_so_far = global_best_cost
#-TODO eliminate dependence on logdir
img_h, img_w = self.img_shape[:2]
decoded = np.empty((img_h, img_w, 3), dtype = np.int)
fitness_ext.decode_rectangles(global_best, decoded)
save_array_as_image(decoded, '%s/%04d.png'%(logdir, iteration))
def test_mutate():
mutators = [g.mutate_shape, g.mutate_color, g.mutate_reorder,
g.mutate_position, g.mutate_noise]
shape = (len(mutators) + 1, 4)
ind = indfactory.build()
decoded = np.empty(IMG_SHAPE, dtype = np.int)
f.decode_rectangles(ind.data, decoded)
orig_decoded = decoded.copy()
draw_in_grid(decoded, shape, 0, 0)
for row in xrange(1, shape[0]):
for col in xrange(shape[1]):
mutated = mutators[row - 1](ind)
# mutated = g.mutate(ind)
f.decode_rectangles(mutated.data, decoded)
draw_in_grid(decoded, shape, row, col)
def test_mutate():
mutators = [
g.mutate_shape, g.mutate_color, g.mutate_reorder, g.mutate_position,
g.mutate_noise
]
shape = (len(mutators) + 1, 4)
ind = indfactory.build()
decoded = np.empty(IMG_SHAPE, dtype=np.int)
f.decode_rectangles(ind.data, decoded)
orig_decoded = decoded.copy()
draw_in_grid(decoded, shape, 0, 0)
for row in xrange(1, shape[0]):
for col in xrange(shape[1]):
mutated = mutators[row - 1](ind)
# mutated = g.mutate(ind)
f.decode_rectangles(mutated.data, decoded)
draw_in_grid(decoded, shape, row, col)
def test_xover():
xovers = [g.crossover_unif, g.crossover_arith]
pa = indfactory.build()
ma = indfactory.build()
decoded = np.empty(IMG_SHAPE, dtype = np.int)
shape = (len(xovers) + 1, 10)
f.decode_rectangles(pa.data, decoded)
draw_in_grid(decoded, shape, 0, 0)
f.decode_rectangles(ma.data, decoded)
draw_in_grid(decoded, shape, 0, 1)
for row in xrange(1, shape[0]):
for col in xrange(shape[1]):
kid = xovers[row - 1](pa, ma)
f.decode_rectangles(kid.data, decoded)
draw_in_grid(decoded, shape, row, col)
def test_xover():
xovers = [g.crossover_unif, g.crossover_arith]
pa = indfactory.build()
ma = indfactory.build()
decoded = np.empty(IMG_SHAPE, dtype=np.int)
shape = (len(xovers) + 1, 10)
f.decode_rectangles(pa.data, decoded)
draw_in_grid(decoded, shape, 0, 0)
f.decode_rectangles(ma.data, decoded)
draw_in_grid(decoded, shape, 0, 1)
for row in xrange(1, shape[0]):
for col in xrange(shape[1]):
kid = xovers[row - 1](pa, ma)
f.decode_rectangles(kid.data, decoded)
draw_in_grid(decoded, shape, row, col)
def decode(self, particle):
fitness_ext.decode_rectangles(particle.position, self.decoded)
def decode(self, individual):
fitness_ext.decode_rectangles(individual.data, self.decoded)
def decode(self, individual):
fitness_ext.decode_rectangles(individual.data, self.decoded)